.png){kind=icon}
.png){kind=icon}
Return
State Transition Simulated Annealing Algorithm for Discrete-Continuous Optimization Problems
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2908961.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A simulated annealing (SA) algorithm is an effective method for solving optimization problems, especially for combinatorial optimization problems. However, SA algorithms rely heavily on the iterative mechanism of the neighborhood structure. When the scale of the problem becomes larger, the convergence rate is slow and it is difficult to reach the global optimal solution. In this paper, seven special intelligent operators are introduced to a SA algorithm, and therefore, a new state transition SA (STASA) algorithm is proposed for combinatorial optimization and continuous optimization problems. The performance of the STASA algorithm is tested on the traveling salesman problem and some continuous function optimization problems. The experimental results show that the proposed algorithm is efficient and reliable.
Keywords:
State transition
simulated annealing algorithm
combinatorial optimization
continuous optimization problem
traveling salesman problem
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.